xdge | Blog

The modern workday is defined by motion without momentum. Teams move across Slack, Confluence, JIRA, Salesforce, Notion, Drive, and Gmail—chasing context, piecing together updates, and stitching progress manually from fragmented information. The cost is not just measured in hours lost; it is a deeper erosion of focus, creativity, and forward momentum.

Ayraa envisions a better foundation for modern work: a system that continuously defragments & organizes your workspace for you through pre-defined workflows.

We're excited to introduce Deep Research Workflows, autonomous, intelligent workflows that continuously transform scattered knowledge - from chats, emails, tickets, and documents - into structured synopses custom-prepared for you and delivered precisely where and when you need them.

Workflows That Think Forward

A Deep Research Workflow is a persistent, self-operating system built to mirror how employees compile information from scattered sources but now powered by a reasoning AI model that can search, collate, and synthesize knowledge on your behalf. You describe the outcome you need—whether a weekly team recap, a sales pipeline report, or a stakeholder newsletter based on workspace activity—and Ayraa orchestrates the steps to deliver it autonomously.

The process is simple: you define the goal once in natural language. Ayraa unpacks it into logical actions and reasons across your workspace in real-time and carries the work forward on a schedule you control. There are no fragile automations to maintain, no brittle triggers to fix. The system runs reliably across Slack, JIRA, Confluence, Salesforce, Notion, Drive, and Gmail, adapting to changes and surfacing the most critical insights automatically.

It's research that doesn't just happen once — it happens reliably, rhythmically, and intelligently.

How It Works Behind the Scenes

Designing an Ayraa workflow is as simple as writing out what you want. There's no need to draw flowcharts or write any code. You speak your workflow into existence by giving the AI a detailed description of the task, just like you would explain it to a colleague. Because the system uses advanced reasoning models that understand complex instructions, you can be very specific and nuanced in your request. Ayraa will understand your intent and figure out how to execute it step by step.

Objective

You are an excellent AI assistant adept at following Slack threads and understanding if someone is waiting on a person for a response.

Please search Slack with the approach detailed below and capture summaries of all places where people are waiting on me. Please also share the excerpt of what exactly was asked, and your 1-2 sentence summary of what is needed from me based on the overall context.

Here's a sample workflow excerpt for a template that reads your entire Slack activity and finds any places where you have someone waiting for an action or task.

Search steps

The way to search Slack would be to look at the time period selected (use last 24 hours if nothing is selected) and search for the name [insert your name here] (my name) in the Slack search. For example, if today is Apr 4th 2025, then you would search for after:2025-04-02 John to mean all messages that had the word "John" in it and that were posted after April 2nd – which includes Apr 4th (today) and to be safe, all of Apr 3rd as well.

Then please extract the threads where you found these matches, and analyze those threads to figure out what is needed from me (as explained in the objective).

Reporting

Please create a report with an Executive summary, and then a list of topics (clear headlines for each thread you found where just reading the title allows me to know what the thread/topic was about - use simple conversational English and not a "word salad" that is hard to follow.

Under each topic then, provide a summary of what is needed from me with an excerpt of the exact message and who sent it.

At the end, have a list of references cited above with Slack hyperlinks to each thread where I am needed.

This natural language approach means anyone can set up an advanced workflow without technical barriers. Suppose you set a workflow to "Summarize the week's product updates every Friday at 5pm." In that case, Ayraa will:

• scan JIRA for features implemented, bugs fixed, and any launches,
• extract the top themes and keywords from these,
• then pull high-signal conversations from Slack on these topics and keywords,
• scrub, score, and organize the content extracted from JIRA & Slack, synthesizing insights & gathering summaries,
• format polished release notes,
• and send them directly to your team's channel or inbox — without you lifting a finger.

And it does this every week without needing reminders, re-prompts, or maintenance.

The knowledge synthesis goes beyond surface-level scraping. The reasoning model creates well-thought-out plans, and the autonomous execution allows the AI to run for a long time and go deep in your workspace - iterating, if needed.

24/7 Deep Research on Autopilot

Once you define a workflow, Ayraa’s agent runs with it on autopilot.

Think of it as a tireless digital researcher that works around the clock on your behalf. Need a competitive analysis or an incident report first thing in the morning? The agent can be working on it overnight. These workflows introduce always-on helper agents to your workspace that can easily 10× your productivity by handling work continuously across limitless use cases.

Importantly, the agent doesn’t just run once – it can be set to run whenever needed or even continuously watch for new information. You don’t have to babysit it. After you hit “go,” the AI will autonomously carry out the task from start to finish. It’s like putting your research on cruise control: the heavy lifting happens in the background, without constant oversight. By the time you check in, the research is done and neatly packaged. This frees you from the constant juggle between tools and tasks, effectively eliminating a lot of busywork.

Fine-Grained App Control and Integration

Ayraa workflows operate across all your work apps – but you remain in control of where and how they search for information. With each workflow, you can specify exactly which applications or data sources the AI should use. For example, you might direct an agent to pull data only from your project management tool and database, or to search both your Slack messages and Google Drive documents. You can also provide per-app instructions to guide the agent: for instance, tell it to look in a particular Slack channel, or to ignore older documents in Confluence.

Under the hood, the AI translates your natural language instructions into targeted queries for each app. It knows how to use the APIs and search capabilities of tools like Slack, Gmail, Jira, or Salesforce to find relevant data. By controlling the app scope, you ensure the agent has access to the right context and nothing extraneous. This fine-grained control means the workflow’s output is both relevant and compliant with any data boundaries you have. In short, you get the benefits of deep integration with your tech stack without handing over the steering wheel entirely – you set the boundaries, and the AI executes within them.

Multi-Step Reasoning and Execution

Complex tasks often require multiple steps and careful reasoning, and Ayraa’s agents are built to handle exactly that. When a workflow runs, the AI doesn’t just do one simple search and stop. It mimics human-like reasoning: it will think through the task, break it down into sub-tasks, and plan a sequence of actions to achieve the end goal. After each step, the agent pauses to reflect on the results it just got. It checks whether those results are relevant and decides what to do next. This might involve refining a search query, looking up a definition, or branching out to another data source – whatever the plan requires.

This iterative, multi-step approach means the agent can tackle complex research questions that a single query can’t solve. It can gather information from multiple places, cross-reference facts, and adjust its strategy if new information changes the picture. And it does all of this tirelessly, without rushing or skipping steps. Workflows can run for minutes or even hours, methodically going through the plan just like a diligent human researcher would. The result is a thorough job: by the end, the AI has compiled and synthesized information from many sources, having effectively “thought through” the problem in a logical way.

Collaboration and Template Sharing

Ayraa makes it easy to get started with workflows by providing dozens of templates for common use cases. You don’t have to start from scratch if you don’t want to. For example, there might be a template for “Weekly Sales Report” or “Product Release Notes” already available. You can pick a template that’s close to what you need and then customize it to fit your specific requirements. Every workflow can be tweaked – you can add or remove steps, change the data sources, or refine the instructions – so the outcome is exactly right for you.

Once you have a workflow that works well, you can share it with your team. Collaborate and share workflows just as you would share a document or script. Your teammates can run the same workflow or further modify it for their needs. This means best practices in your organization can spread quickly. If one person figures out a great automated research process (like a perfect weekly engineering recap), everyone else can benefit from it in just a few clicks. The result is a more streamlined operation across the board, as people aren’t reinventing the wheel for recurring tasks.

Scheduling and Automation of Recurring Tasks

One of the most powerful features of Deep Research Workflows is the ability to schedule them to run automatically. You can set up a workflow to execute at a specific time or on a recurring schedule – for example, every day at 7:00 AM or every Friday afternoon. This is ideal for routine reports and ongoing research tasks. Ayraa gives you precise control over when a workflow runs, so the results are ready exactly when you need them, without manual intervention.

Consider the advantage of this scheduling: your Monday morning team summary can be prepared by 6:00 AM Monday, waiting in your inbox when you start your day. A nightly operations health check can run at midnight and Slack you the highlights by the time you wake up. Because the agent is truly 24/7, it doesn’t matter if these tasks need to run outside of working hours. You’ll get the benefit of up-to-date information delivered on your schedule. In short, automation plus scheduling means important insights are never late or forgotten – they arrive like clockwork.

Detailed Reports with Custom Formatting

The end product of a deep research workflow is typically a detailed report or summary, and Ayraa ensures those reports are polished and easy to read. The AI can output findings in a structured format that you define ahead of time. You might want bullet points, tables of key data, or a narrative summary with section headings – these can be configured as part of the workflow. With pre-configured formatting guidelines, every report comes out consistent and professional-looking without any extra effort.

What’s more, Ayraa’s agents do more than just compile raw data – they highlight the insights that matter. Because the AI is doing reasoning along the way, it can include context and explanations in the report, not just dump figures. For example, instead of only listing sales numbers, a report might add, “Sales increased 5% this week, likely due to the launch of Project X,” if it found that insight during its analysis. The workflow essentially writes the report for you, following your formatting preferences and injecting automated insights. This means you get immediate value from the results – they’re presentation-ready and often come with the story behind the data, not just the data itself.

Having a great report isn’t useful if you forget to check it, so Ayraa makes delivery convenient. The platform delivers your workflow results wherever you already work – that could be your email inbox, a Slack channel or direct message, or within the Ayraa app itself. You choose the delivery method that fits your routine. For instance, you might set a competitive intelligence report to be emailed to you and your team lead, while a daily engineering summary could be posted to a private Slack channel every evening.

This multi-channel delivery turns Ayraa into something like a 24/7 executive assistant. The information you need finds you, rather than you having to go look for it. If you live in Slack, you’ll see the updates right alongside your other conversations. If you prefer email, the report will show up as a nicely formatted message. And for deeper dives, you can always view the full details in Ayraa’s app. The key point is that the insights are integrated into your normal workflow and tools, so staying informed becomes effortless.

How Teams Are Using Workflows

Ayraa’s Deep Research Workflows are versatile and can be applied in virtually any domain. Here are a few concrete examples of how different teams use these AI-powered workflows:

• Engineering Reports: Engineering managers can automate daily or weekly status reports. For example, a workflow can gather updates from source code repositories, issue trackers, and team chat. The agent might list new code changes, highlight completed tickets, and flag any blockers. By morning, the entire engineering team has a synthesized report of what happened in development – without anyone manually compiling it.
• Release Notes Generation: Product teams often spend time writing release notes for new features or updates. Ayraa can handle this by collating information from commit messages, pull request descriptions, and project management boards. A workflow can automatically produce draft release notes whenever a new version is ready, complete with a list of new features, improvements, and bug fixes – all formatted and ready to review or publish.
• Sales Signals and Insights: Sales teams can set up agents to watch for important signals, like big deals moving through the pipeline or notable customer activities. A workflow might monitor the CRM for any high-value opportunities that changed status, scan inbound emails for key customer inquiries, and check news sources for mentions of target accounts. It then delivers a daily brief highlighting things like “Lead X became a qualified opportunity” or “Client Y was mentioned in the news today,” giving the sales team actionable intelligence without anyone digging through data.
• Operations Recaps: Operations and executive teams benefit from regular summaries of business health. An operations recap workflow can pull key metrics from various systems – finance software, inventory databases, support ticket logs, uptime monitors, etc. – and compile them into a concise overview. The AI might note, for instance, that “Customer support tickets dropped 10% this week” or “Inventory levels are within normal range.” These recaps ensure leadership is always up to speed on the state of the business, and they’re generated automatically at whatever interval makes sense (daily, weekly, monthly).

Each of these use cases demonstrates the core value of Ayraa’s Deep Research Workflows: time-consuming information gathering and analysis can be delegated to an intelligent agent. Whether it’s an engineer, a salesperson, or an executive, everyone gets to reclaim time and make better decisions because the right information is delivered to them with minimal effort.

Wherever momentum depends on connected knowledge, Ayraa's Workflows quietly take the weight.

A New Paradigm in Workspace Knowledge Management

While modern workspaces are scattered and disorganized, Ayraa’s Deep Research Workflows bring structure that lasts. Set them once, and they run on a schedule—continuously organizing and compressing your digital workspace into clear, distilled reports and insights.

Instead of digging through chat threads or running manual searches, you define the outcome once. Ayraa handles the rest—pulling data from across your tools, synthesizing it, and delivering exactly what you need.

Ayraa takes in all the raw data and pulls out what’s actually useful—clear, timely insights that help you move forward.

The result? Knowledge that’s not just organized—but always ready, always relevant, and always waiting for you.

What Comes Next

We’re not stopping with workflows. We’re building an entire ecosystem of plug-and-play use cases—modeled on how real teams actually operate. From product sprints to pipeline reviews, leadership recaps to customer intelligence, every workflow is designed to save time, reduce noise, and shift how work gets done.

Soon, this won’t feel like a feature. It’ll feel like the default.

We believe this is the future of work: proactive, composed, and quietly intelligent.
And Ayraa brings that future to you—now.

Welcome to a workday that works for you.

We are excited to introduce our first agentic knowledge assistant - your 24/7 Salesforce analyst!

In this write-up, we share details of how we built this agent, along with our framework for extending it to other connectors such as JIRA, Zendesk, and Gmail. We are also introducing "Text to SQL" structured query language capabilities to our Salesforce connector.

Problem Statement

When we first built our Salesforce connector, we implemented advanced fuzzy keyword and semantic search capabilities. While powerful, we quickly discovered our customers needed more. They asked complex, parametric questions that our search couldn't handle effectively.

Our existing system handled basic Salesforce searches effectively, particularly those focused on finding specific opportunities using keywords within our indexed data (title, content, metadata). However, it struggled with more complex user queries that:

Involved relationships between Salesforce objects (Accounts, Opportunities, Contacts, and Tasks)

Examples:

"Show me all opportunities for the United Oil account."

"What tasks are associated with the United Oil account and are due this week?"

Required filtering on specific field values

Examples:

"Show me all opportunities closed won in the last week."

"What are my overdue tasks?"

Needed information not in our search index

Examples:

"What are the details on John Doe?" (Contact details)

"Who is the account owner for Acme Corp?"

Needed a broad search across multiple objects/fields, then filtering

Examples:

"Find all opportunities related to 'renewable energy' that are in 'Negotiation' stage."

"Show me opportunities related to companies or people matching 'cloud solutions'"

These limitations prevented users from asking natural, intuitive questions about their Salesforce data. Additionally, in some cases, as there was no keyword or semantic meaning to search for in the first place, the search would lead to irrelevant retrieval noise in our RAG pipeline.

Enter Text-to-SQL: Precision meets natural language

To solve this, we're introducing Text-to-SQL capabilities in our Salesforce connector. This feature allows our agent to translate natural language questions into precise SQL queries, giving you more control than ever on your search scope.

Instead of using our own SQL database, we picked Salesforce's powerful SOSL (Salesforce Object Search Language) and SOQL (Salesforce Object Query Language) APIs. This allows us to avoid crawling years' worth of Salesforce records & still provide powerful queries over all of the data in seconds.

We will now get into the details of these APIs and how to use them.

Why SOSL and SOQL?

Here's a short crash course on these powerful APIs from Salesforce.

SOSL (Salesforce Object Search Language)

Best for: Broad text searches. Imagine a Google search within Salesforce.

Use it when: You don't know exactly where the information is (which object or field) or you need to search across many objects at once.

Example:

FIND {renewable energy} IN ALL FIELDS (finds "renewable energy" anywhere).

SOQL (Salesforce Object Query Language)

Best for: Structured queries with precise filtering and relationships. Like a particular database query.

Use it when: You know exactly which object and fields you need, and you need to filter based on specific values or relationships.

Example:

SELECT Id, Name FROM Opportunity WHERE StageName = 'Closed Won' AND CloseDate = THIS_YEAR (finds opportunities that are closed won this year).

Sometimes you need both!

Broad Search + Narrowing Down: Use SOSL to find a set of possible records, then use SOQL to filter those records further based on criteria that SOSL can't handle.

Example:

Query: "Find all opportunities related to renewable energy in the negotiation stage."

SOSL: FIND {renewable energy} IN ALL FIELDS RETURNING Opportunity(Id, Name, StageName) (broad search).

SOQL: SELECT Id, Name, StageName FROM Opportunity WHERE StageName = 'Negotiation' AND Id IN (<ids from SOSL>) (filter by stage).

Thankfully, modern LLMs are trained on the language powering these APIs, allowing us to generatively create them on the fly based on user queries.

Enter 24/7 Agent for your Salesforce data

We now describe our agentic search framework, which allows users to speak to their business data. By implementing an AI agent as an intermediary between user queries and various search tools, we've created a system that intelligently determines the most efficient strategy to retrieve relevant information. The agent can also easily be extended to other CRMs and apps like Zendesk, Gmail, Calendar, etc.

Agent Architecture

The agent sits at the forefront of the architecture, acting as an intelligent & flexible middleware between user queries and the backend infrastructure. This design allows for efficient query routing and seamlessly extending the agent's capabilities in the future.

Core Tools

The framework consists of four primary tools available to the agent:

1. Salesforce Object Search Language (SOSL) API from Salesforce
2. Salesforce Object Query Language (SOQL) API from Salesforce
3. Proprietary indexed and embedded vector data for workspace content
4. Native text-search API for historical text search from Salesforce

We document these tools in detail so that the agent can understand when to use them and, if needed, how to use them effectively.

Agentic Workflow

We then give the Agent a high-level workflow to follow for every user query, but with flexibility driven by its reasoning on how to navigate the workflow. The sequence is:

1. User Intent Analysis

The agent initiates by decoding the user's intent through multi-layered analysis:

• Domain Classification: Determines whether the query relates to Salesforce objects (e.g., accounts, opportunities) or requires external tools.
• Tool Requirement Assessment: Evaluates which Salesforce search tool or mix of tools are necessary based on the user's intent and search scope.

This phase resolves ambiguities early, ensuring downstream processes align with the user's goals.

2. Tool Selection

Leveraging insights from intent analysis, the framework selects optimal tools and strategies:

• Requirement-Tool Matching: Maps query parameters (e.g., date filters, relationship mapping) to available tools.
• Strategy Optimization: Prioritizes execution order—for example, running parametric date filters before semantic keyword searches to narrow the dataset.
• Finalization: Confirms tool sequence and prepares for execution.

This phase eliminates redundant tool usage and ensures resource efficiency.

3. Tool Execution

Once the intent is translated into exact tool or mixture of tools to use, the agent moves towards orchestrating the execution of these tools. The agent has to command our backend code to do this via precise instructions. The agent does this by executing the following steps:

• Entity & relationship extraction: Based on the user query, the agent selects Salesforce objects (accounts, opportunities, tasks & contacts) & the relationships between them ("opportunities linked to Account X")
• Parametric search scope extraction: Likewise, the agent constructs the exact set of criteria the user has specified in terms of date-ranges, personnel, keywords, amounts or other fields.
• Query Construction & Submission: The agent then speaks the language of each of the tools it needs to use. It translates the above entities, relationships, and search filters into the exact parameters, configurations, and/or queries to send to the backend to execute these tools (Salesforce SOSL and SOQL API, Elasticsearch). We use a simple JSON-formatted payload to explain this to the backend.
• Response Handling: The agent then waits for the backend to respond. Once the responses start coming from the various tools, the agent collates responses, validates results, resolves errors & prepares an appropriate response for the user based on the combined response from the tools used.

The key innovation in any agentic framework is that the backend tools are decoupled from the workflow, and the agent is given some level of flexibility to use its reasoning in orchestrating the worfklow and responding to the user accordingly.

Key Benefits

There are several benefits from this release of our Salesforce connector.

• Text to SQL querying via SoSL and SoQL APIs enable extremely powerful analysis, but now possible conversationally
• Analytics become accessible - 24/7 across the sales and management organizations
• Unlimited access to historical data going back years
• Low cost and operational footprint
• Foundation for Agentic search, actions & workflows for various workplace apps

Conclusion

We are excited to introduce this next-generation Salesforce connector & hope this write-up helps explain the technical details behind how to create it in-house.

For more information about implementing this connector or to schedule a demonstration, please get in touch with our sales team via our website - www.ayraa.io

As we close out 2024, we want to share Ayraa's remarkable journey in revolutionizing enterprise search and knowledge discovery. What started as a vision to help employees feel connected to workplace knowledge has evolved into a comprehensive platform that democratizes enterprise search while pushing the boundaries of what's possible with modern AI technology.

The Evolution: From Communication Platform to Search & Knowledge Assistant

Ayraa's story began in late 2021 with a vision to help employees feel connected to workplace knowledge. Initially, we focused on building an uncluttered, reliable communication platform overlaying existing tools like Slack and similar applications. The ChatGPT revolution in late 2022 catalyzed our transition. By early 2023, we had pivoted from a communication-centric approach to building a search and knowledge discovery engine.

This transformation culminated in our December 2023 launch on Product Hunt and debut at TechCrunch Disrupt, where we received exceptional feedback. The foundation was set for what would become a transformative 2024, during which we built 90% of our current platform.

"Recent Mode" Innovation: Balancing Gen AI with Coverage at an Accessible Cost

In 2024, we introduced the "Recent" mode as one of our most practical innovations, embodying our commitment to democratizing enterprise search. We developed this hybrid approach to combine the following:

• Leveraging each app's native Search API for near-zero operational costs for historical data going back years across all apps
• Advanced indexing and vector embedding for recent data (90 days for paid users, 14 days for free users)

In the Recent mode, users work with a recent cache of their workspace to cover 95%+ of their daily knowledge search and discovery needs. Using modern Gen AI capabilities, the Recent mode provides:

• Semantic search functionality to search by semantics/meaning (e.g., sneakers vs shoes)
• Fuzzy keyword matching, which is resilient to typos (e.g., sneakres vs sneakers)
• Blazing-fast, "sub-3-second" search results with immediate streaming of answers

Meanwhile, for historical searches that go back years, users can leave the Recent mode, pick All Time or specific date ranges, and benefit from API-based searches. For date ranges not covered by indexed and embedded content, you lose Gen AI capabilities but still have historical coverage across all apps. 

This innovation enables Ayraa to maintain a disruptively low pricing while delivering high-quality Gen AI capabilities for 95%+ of the use cases.

Search 2.0: Making Workspace Knowledge Accessible

In 2024, we transformed search into a powerful default landing place for users, making your workspace more accessible than ever. We enhanced functionality with:

• Click-to-query or summarize functionality for every search result - ensuring docs, files, long threads, or complex tickets are not only searchable across the workspace but, once found, can be easily understood
• Assist-like summaries at the top of search results to provide quick answers when users don't need to look at all results
• Dual-view options: Grid view for app-specific results and List view for confidence-based ordering

Comprehensive Connector Coverage

In 2024, we expanded our integration capabilities to include:

• 15 specialized application connectors (Notion, Confluence, Salesforce, OneDrive, Box, etc.)
• Slack bot for seamless in-platform interaction
• Browser extension that serves as a web co-pilot for the millions of web pages, including Wikipedia pages, blogs, Reddit threads, etc.
• Complete Microsoft ecosystem support (SharePoint, OneDrive, Outlook Calendar, Teams, etc.)

Expanding Knowledge Capture & Search to Meetings

We launched our Meetings app in mid-2024 with support for all major meeting applications (Zoom, Slack, Microsoft Teams, Google Meet).

We initially launched with a browser extension approach that leveraged closed captioning in web interfaces of meeting platforms. While highly effective for specific use cases, this approach had limitations. Users often preferred native desktop apps over web interfaces, and the browser-based solution couldn't serve users who couldn't attend meetings personally.

This led us to develop our bot-based approach, which became our primary solution. The bot was designed to synchronize with users' calendars to automatically attend meetings on their behalf, providing a more seamless experience. This approach solved both the native app limitation and the meeting attendance constraint.

However, we recognized that bot attendance sometimes created unnecessary friction - each bot has to "knock" to join meetings, potentially disrupting hosts and participants. In 2025, we will build a botless alternative to address such needs. This hybrid approach allows the bot-based approach to serve scenarios where users are absent, while the new botless recording will cater to use cases where the user prefers uninterrupted meeting flows.

Discovery & Insights Engine Debut

In 2024, we developed our discovery engine, powered by what we call "reverse GPT," representing a fundamental shift in how workplace content is processed and understood. As we crawl through platforms like Slack, we don't just index and vectorize content – we intelligently classify it into meaningful categories such as process changes, announcements, escalations, releases, milestones, scrum notes, and meeting minutes.

The Insights app, launched in beta in 2024, was developed to treat workspace activity as a continuous stream of events. By using text-to-SQL technology, we enabled powerful queries across this event stream, extracting insights that aren't possible through traditional snapshot-based keyword or semantic searches. This allowed users to ask questions like:

• "What did I work on today?"
• "What happened in the workspace while I was gone last week?"
• "What Jiras did John work on?"
• "Show me all company announcements from the past 30 days."

Looking ahead to Insights 2.0 in mid-2025, we're exploring two promising approaches to enhance our querying capabilities:

• Multiple SQL-based phased queries
• Spreadsheet-based querying that replaces some layers of SQL

This evolution will transform Ayraa from a pure search platform into a powerful analytical tool, enabling users to derive meaningful insights from any email, CRM, ticketing system, or chat application by converting notable activities into queryable events. We've already seen strong results in specific use cases and are excited to expand these capabilities across our platform.

Collections: Building a Personal or Collaborative Second Brain

In early 2024, we introduced Collections, representing a significant advancement in how teams organize and share institutional knowledge. This technology was developed to enable teams to build a trusted, organized second brain that can be easily replicated and shared across the organization.

Collections was developed with a comprehensive approach to knowledge management:

• Flexible Content Integration: We built the ability for users to create collections through multiple sources - uploading official documents (Word, PDFs), linking trusted resources from supported platforms (Notion, Confluence, Google Docs), creating information cards, or directly adding bookmarked websites through drag-and-drop functionality
• Smart Search & Query Integration: We integrated Collections seamlessly with Ayraa's search capabilities, allowing users to constrain their queries to specific curated knowledge sets. This meant searches and assist queries could focus exclusively on verified, curated content rather than searching the entire workspace
• Enterprise-Ready Verification: We implemented an expert validation workflow where subject matter experts can verify content and timestamp its relevance, ensuring collections maintain their accuracy over time

The impact of Collections has been particularly notable across different organizational roles:

• Executive Teams: Streamlining access to strategic plans, OKRs, and compliance documentation
• Engineering & Product: Centralizing technical guides and project specifications to reduce repetitive questions
• HR Teams: Revolutionizing employee onboarding through organized training and policy documentation
• Sales Enablement: Providing 24/7 access to product documentation, pricing sheets, and competitive analysis

The power of Collections was demonstrated in its ability to transform individual expertise into shared organizational knowledge, ensuring teams have immediate access to accurate, current information without hunting through multiple platforms or channels.

Go Links: Simplifying Resource Access

In 2024, we developed Go Links to address a critical need we identified: simplifying access to frequently used enterprise resources. We built Go Links to transform complex URLs into human-friendly shortcuts anyone can remember and share.

The technology became foundational to workplace knowledge sharing, with teams across organizations adopting shortcuts like:

• "go/benefits" for HR's benefits documentation
• "go/sales-assets" for sales enablement resources
• "go/engineering" for technical processes and release docs
• "go/support" for customer service playbooks

This elegant solution to resource organization was developed to complement our broader search capabilities while providing quick access to frequently used resources. Teams could now organize and share their knowledge through intuitive, standardized shortcuts rather than wrestling with lengthy, complex URLs.

The Slack Co-pilot Experience

Throughout 2024, we focused on making Ayraa seamlessly accessible within Slack, where many users spend their workday. We evolved our Slack bot beyond simple search functionality to become a faithful workplace co-pilot, enabling users to harness Ayraa's full capabilities without context switching.

The impact was transformative for team productivity. We enabled users to mention Ayraa within any Slack conversation to instantly access workplace knowledge, turning the bot into a 24/7 consultant who provides contextual clarifications during ongoing discussions. This proved particularly valuable during decision-making processes, where teams could quickly reference historical context or related documentation.

We developed our thread summarization capability as a crucial time-saver - rather than reading through lengthy discussions, users could get key points or ask specific questions about the thread's content right where they work.

Moreover, we equipped the bot with the ability to detect and highlight stalled discussions, helping teams maintain momentum on essential conversations that might otherwise have been forgotten.

Perhaps most impactfully, we enhanced our discovery (Insights) engine to proactively bring essential updates to users through the Slack bot. By identifying and delivering crucial escalations, milestone achievements, and releases directly to relevant team members, we helped ensure that critical information doesn't get lost in the noise of busy workspaces.

Our Slack integration developments in 2024 included:

• Instant workplace search within Slack
• Thread summarization and querying
• 24/7 contextual workspace assistance
• Notifications of significant updates across your workspace
• Stalled discussion identification and follow-up

The Browser Co-pilot Experience

Early in 2024, we developed our browser extension, initially designed to help users rapidly summarize and search through lengthy web content like Wikipedia articles, blogs, and Reddit discussions.

Throughout the year, we evolved the extension into a faithful browser co-pilot. Built on standard HTML/CSS parsing capabilities, we enabled it to seamlessly process content from millions of websites. The technology proved particularly valuable for:

• Web research - allowing users to summarize/query pages and save content directly to their Collections
• PDF documents - extracting and analyzing text from vector-based documents without specialized readers
• YouTube videos - providing instant transcript access, summaries, and timestamp-based navigation

Quality at Scale: Building Trust Through Automation

In 2024, we invested heavily in automated quality systems to ensure reliability at scale. A cornerstone of this investment was our automated regression suite for Assist, which we developed to evaluate a comprehensive set of search queries and responses daily. This system was designed not only to catch potential regressions in search quality and answer accuracy but also to help measure improvements as we fine-tune our platform's weights and configurations.

To measure our RAG (Retrieval Augmented Generation) effectiveness, we integrated the open-source Tonic Validate tool into our testing framework. This integration provided quantifiable metrics for response quality, allowing us to make data-driven decisions about our search and assist features.

Enterprise-Grade Security Focus

Security remained core to our DNA, with our founding team's experience from McAfee. 2024 achievements included:

• SOC 2 Type 2 compliance across all five trust principles
• CASA Tier-2 certification from PWC
• Grade A (100% pass) in third-party penetration testing by Halo Security

Looking Forward

As we enter 2025, we're excited to continue innovating in enterprise search and knowledge discovery. Our commitment to democratizing access to workplace knowledge remains stronger than ever, supported by our technical innovations and user-centric approach.

This journey wouldn't be possible without our users' trust and feedback. We look forward to bringing even more innovations to workplace knowledge discovery in 2025.